wasserstein contrastive representation distillation

Sun, Z. Gan, Y. Cheng, Y. Fang, S. Wang and J. Liu "Contrastive Distillation on Intermedi- Chenyu You Nuo Chen and Yuexian Zou "Self-supervised contrastive cross-modality representation learning for spoken question answering" 2021. Then, to obtain a generalized representation, a contrastive learning strategy is developed to emphasize liveness-related style information while suppress the domain-specific one. Algorithm 1 The proposed WCoRD Algorithm. [5:25] Tent: Fully Test-Time Adaptation by Entropy Minimization. 70. Pan Yang, Xin Cong, Zhenyu Sun and Xingwu Liu. The model outputs colors in the the CIE Lab . Tian et al. 7. We propose Wasserstein Contrastive Representation Distillation (WCoRD), which leverages both primal and dual forms of Wasserstein distance for KD. 引言 Knowledge Distillation在近几年得到了广泛的关注和研究。 . Contrastive learning, a sub-area of self-supervised learning, has recently . 002 (2022-02-14) A Novel Transfer Learning Framework with Prototypical Representation based Pairwise Learning for Cross-Subject Cross-Session EEG-Based Emotion Recognition. The dual form is used for global knowledge transfer, yielding a contrastive learning objective that maximizes the lower bound of mutual information between the teacher and the student networks. ICLR 2020. Wasserstein Contrastive Representation Distillation 作者： Liqun Chen / Zhe Gan / Dong Wang / Jingjing Liu / Ricardo Henao / Lawrence Carin 发表时间：2020 Examples include distilling a large network into a smaller one . cvpr 2021; Computation-Efficient Knowledge Distillation via Uncertainty-Aware Mixup. Self-Supervised Learning (SSL) is a pre-training alternative to transfer learning.Even though SSL emerged from massive NLP datasets, it has also shown significant progress in computer vision. 1: Input: A mini-batch of data samples {xi, yi}i=1. International Joint Conference on Artificial Intelligence, 2021. Xu, Guodong et al. 69. Wasserstein Contrastive Representation Distillation: Supplementary Material. "Liqun Chen et al. : Wasserstein Contrastive Representation Distillation. : Wasserstein Contrastive Representation Distillation. 正负样本的构建： paper所描述的是1个正样本对应k个负样本。. Finally, the representations of the correct assemblies are used to distinguish between living and spoofing during the inferring. Curriculum-Meta Learning for Order-Robust Continual Relation Extraction. ICLR 2020. Problem defini. Wasserstein Contrastive Representation Distillation. Learning disentangled representations with the Wasserstein Autoencoder (840) Benoit Gaujac, . Lawrence CarinJames L. Meriam Distinguished Professor of Electrical and Computer Engineering. Chenyu You Nuo Chen and Yuexian Zou "MRD-Net: Multi-Modal Residual Knowledge Distillation for Spoken Question Answering" IJCAI 2021. The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student network, with the latter being more compact than the former. Wasserstein GAN (WGAN) Improved Training of Wasserstein GANs (WGAN-GP) Conditional Generative Adversarial Nets: Semantic Image Synthesis with Spatially-Adaptive Normalization (GauGAN/SPADE) Neural Style Transfer: Image Style Transfer Using Convolutional Neural Networks (NST) Knowledge Distillation python 3.7; pytorch 1.3.1 . To address the above issues, this paper proposes a novel algorithm called RSC-Net, which consists of a Resolution-aware network, a Self-supervision loss, and a Contrastive learning scheme. The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student network, with the latter being more compact than the former. "Optimal Rejection Function Meets Character Recognition Tasks. Spatial Contrastive Learning for Few-Shot Classification (164) Yassine Ouali, . Wasserstein Contrastive Representation Distillation . Tian Y, Krishnan D, Isola P. Contrastive representation distillation[C]. 因此 . Zhiqiang Liu, Yanxia Liu and Chengkai Huang. 2: Extract features hT and hS from the teacher and student networks, respectively. Periodic Intra-Ensemble Knowledge Distillation for Reinforcement Learning (14) Zhang-Wei Hong, . [5:00] Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency. Original Pdf: pdf; Keywords: Knowledge Distillation, Representation Learning, Contrastive Learning, Mutual Information; TL;DR: Representation/knowledge distillation by maximizing mutual information between teacher and student; Abstract: Often we wish to transfer representational knowledge from one neural network to another. CRCD: Complementary Relation Contrastive Distillation; WCoRD: Wasserstein Contrastive Representation Distillation; LKD: Local Correlation Consistency for Knowledge Distillation; KDCL: Online Knowledge Distillation via Collaborative Learning; ONE: Knowledge Distillation by On-the-Fly Native Ensemble; Requirements. Semi-Online Knowledge Distillation. Knowledge distillation via softmax regression representation learning. [387] SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution. 【CVPR 2021】基于Wasserstein Distance对比表示蒸馏方法：Wasserstein Contrastive Representation Distillation论文地址：主要问题：主要思路：Wasserstein Distance：基本内容：定义：具体实现：Global Contrastive Knowledge Transfer：Local Contrastive Knowledge TransferUnifying Global and Local Knowledge Tran. While we argue that the inter-sample relation conveys abundant information . We propose Wasserstein Contrastive Representation Distillation (WCoRD), which leverages both primal and dual forms of Wasserstein distance for KD. Then Chen et al. "Liqun Chen et al. cvpr 2021 ; Knowledge Refinery: Learning from Decoupled Label. Knowledge distillation aims to transfer representation ability from a teacher model to a student model. _:ID_b369261bbf1e2182c5245afaa9ee06c1 . 正负样本的构建： paper所描述的是1个正样本对应k个负样本。. notes bibtex. [39] Existing work, e.g., using Kullback-Leibler divergence for distillation, may fail to capture . For instance, two users living wide apart may have different tastes of food. 刻画输出之间的差别不仅仅可以用cross entropy，其他的诸如MSE、KL divergence、JS divergence甚至Wasserstein distance . 这就是文章中应用Contrastive Learning思想的来源。. 1: Input: A mini-batch of data samples fx i;y ign i=1. 2: Extract features h and h from the teacher and student networks, respectively. Wasserstein contrastive representation distillation L Chen, D Wang, Z Gan, J Liu, R Henao, L Carin Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern … , 2021 This, however, leads to mode collapse in the multi-modal setting, when both image and text encoders are end-to-end trainable. Xiaotong Ji, Yuchen Zheng, Daiki Suehiro, and Seiichi Uchida https://t.co/982ke25NhR" We propose Wasserstein Contrastive Representation Distillation (WCoRD), which leverages both primal and dual forms of Wasserstein distance for KD. MetaDistiller: Network Self-Boosting via Meta-Learned Top-Down Distillation Bibliographic details on Wasserstein Contrastive Representation Distillation. Prototype Completion With Primitive Knowledge for Few-Shot Learning . Current Appointments & Affiliations. Vision-and-Language Representation Learning", Neural Information Processing Systems (NeurIPS), 2020 Spotlight, Top 4% among all submissions, SOTA on 6 Vision+Language tasks 2.S. Pages 10355-10362 | PDF. Abstract: The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student network, with the latter being more compact than the former. PDF Shuyang Dai, Zhe Gan , Yu Cheng, Chenyang Tao, Lawrence Carin and Jingjing Liu "APo-VAE: Text Generation in Hyperbolic Space", North American . (2021)" . While trying to im. The . International Joint Conference on Artificial Intelligence, 2021. Recent Papers . PaperView: Generalized Wasserstein Dice Score for Imbalanced Segmentation 其实就是各个样本来源于不同的类别。. Wasserstein Contrastive Representation Distillation . We propose Wasserstein Contrastive Representation Distillation (WCoRD), which leverages both primal and dual forms of Wasserstein distance for KD. Self-supervised representation learning by counting features. Tian Y, Krishnan D, Isola P. Contrastive representation distillation[C]. Fig. Lawrence Carin earned the BS, MS, and PhD degrees in electrical engineering at the University of Maryland, College Park, in 1985, 1986, and 1989, respectively. Liqun Chen, Zhe Gan, Dong Wang, Jingjing Liu, Ricardo Henao, Lawrence Carin. The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) Multi-Source Distilling Domain Adaptation Sicheng Zhao,1∗# Guangzhi Wang,2# Shanghang Zhang,1# Yang Gu,2 Yaxian Li,2,3 Zhichao Song,2 Pengfei Xu,2 Runbo Hu,2 Hua Chai,2 Kurt Keutzer1 1University of California, Berkeley, USA, 2Didi Chuxing, China, 3Renmin University of China, China {schzhao, gzwang98, shzhang.pku}@gmail . 我々は,kd に対するwasserstein 距離の原型と双対型の両方を利用するwasserstein contrastive representation distillation (wcord) を提案する。 二重形式はグローバルな知識伝達に使われ、教師と学生ネットワークの間の相互情報の低境界を最大化する対照的な学習目標を . Enhanced Language Representation with Label Knowledge for Span Extraction. Colorization can be used as a powerful self-supervised task: a model is trained to color a grayscale input image; precisely the task is to map this image to a distribution over quantized color value outputs (Zhang et al. 34. Wasserstein Selective Transfer Learning for Cross-domain Text Mining. Shizun Wang, Ming Lu, Kaixin Chen, Jiaming Liu, Xiaoqi Li, Chuang Zhang and Ming Wu. Add a list of references from , , and to record detail pages.. load references from crossref.org and opencitations.net Wasserstein Contrastive Representation Distillation. S. Varshney, V.K. Wasserstein Contrastive Representation Distillation. Algorithm 1 The proposed WCoRD Algorithm. She also holds an MBA degree from Judge Business School (JBS) at University of Cambridge. Liqun Chen*, Dong Wang*, Zhe Gan, Jingjing Liu, Ricardo Henao and Lawrence Carin "Wasserstein Contrastive Representation Distillation", Computer Vision and Pattern Recognition (CVPR), 2021. 这就是文章中应用Contrastive Learning思想的来源。. b) Contrastive Learning Objective. 3: Construct a memory buffer B to store previous computed features. Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer. Assistant Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering , Pratt School of Engineering 2018. 2021 ICCVPerturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation[pdf] [supp][bibtex]Densely Guided Knowledge Distillation Using Multiple Teacher Assistants[pdf] [supp] [arXiv]Figure 1. 2021. Soft-Label Dataset Distillation and Text Dataset Distillation. Previous approaches focus on either individual representation distillation or inter-sample similarity preservation. The dual form is used for global knowledge transfer, yielding a contrastive learning objective that maximizes the lower bound of mutual information between the teacher and the student networks. The . @inproceedings{, title=Wasserstein Contrastive Representation Distillation, author=Liqun Chen, Dong Wang, Zhe Gan, Jingjing Liu, Ricardo Henao, Lawrence Carin, booktitle=CVPR 2021, year=2021} link Lingyun Feng, Minghui Qiu, Yaliang Li, Haitao Zheng and Ying Shen Peng B, Jin X, Liu J, et al . 4: Global contrastive . Effective Fine-Tuning Methods for Cross-lingual Adaptation. I noticed that there is a paper titled "wasserstein contrastive representation distillation" lately which borrowed most of the idea from this RepDistiller while modifying the distance metric using wasserstein distance. Automated segmentation in medical image analysis is a challenging task that requires a large amount of manually labeled data. arXiv - CS - Machine Learning Pub Date : 2020-12-15 , DOI: arxiv-2012.08674. Distillation的目标 ：在同类别的输出上让Student与Teacher靠近；在不同类别的输出上让Student和Teacher远离。. Wasserstein contrastive representation distillation L Chen, D Wang, Z Gan, J Liu, R Henao, L Carin Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern … , 2021 A food recommender mobile application installed on an edge device might want to learn from user feedback (reviews) to satisfy the client's needs pertaining to distinct domains. [38] Tung F. and Mori G. , Similarity-preserving knowledge distillation, In Proceedings of the IEEE International Conference on Computer Vision (2019), 1365-1374. The detailed implementation of the proposed Wasserstein Contrastive Representation Distillation (WCoRD) method is summarized in Algorithm1. Has anyone implemented Wasserstein Contrastive Representation Distillation I noticed that there is a paper titled "wasserstein contrastive representation distillation" lately which borrowed most of the idea from this RepDistiller while modifying the distance metric using wasserstein distance. . We propose Wasserstein Contrastive Representation Distillation (WCoRD), which leverages both primal and dual forms of Wasserstein distance for KD. Assistant Professor in Biostatistics & Bioinformatics, Biostatistics & Bioinformatics , Basic Science Departments 2021. 【CVPR 2021】基于Wasserstein Distance对比表示蒸馏方法：Wasserstein Contrastive Representation Distillation，程序员大本营，技术文章内容聚合第一站。 Wasserstein Contrastive Representation Distillation [114.24609306495456] We propose Wasserstein Contrastive Representation Distillation (WCoRD) which leverages both primal and dual form of Wasserstein distance for knowledge distillation。 二重形式はグローバルな知識伝達に使用され、教師と学生のネットワーク間の . In 1989 he joined the Electrical Engineering Department at Polytechnic University (Brooklyn . Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models. Retrieving user data comes at the cost of privacy . "Complementary relation contrastive distillation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. The dual form is used for global knowledge . 其实就是各个样本来源于不同的类别。. Let X = {x} denote the set of document bag-of-words.Each vector x is associated with a negative sample x - and a positive sample x +.We assume a discrete set of latent classes C, so that (x; x +) have the same latent class while (x; x -) does not.In this work, we choose to use the semantic dot product to measure the similarity between prototype x and the . We propose Wasserstein Contrastive Representation Distillation (WCoRD), which leverages both primal and dual forms of Wasserstein distance for KD. Dr. Jingjing Liu received the PhD degree in Computer Science from MIT EECS. Wasserstein Contrastive Representation Distillation (WCoRD) is proposed, which leverages both primal and dual forms of Wasserstein distance for KD, yielding a contrastive learning objective that maximizes the lower bound of mutual information between the teacher and the student networks. Wasserstein Contrastive Representation Distillation Liqun Chen, Dong Wang, Zhe Gan , Jingjing Liu, Ricardo Henao, Lawrence Carin One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking Chen, Liqun et al. Spotlight s 5:15-5:55. first propose the contrastive representation distilla-tion framework which regards the representation of the same image from students and teachers as a positive pair in contrastive learning. Ding, Qianggang et al . Dr. Liu was Senior Principal Research Manager at Microsoft, leading a research group in Multimodal AI, centering on Vision Language Multimodal Intelligence, the intersection between Natural Language Processing and Computer Vision. The proposed network is able to learn the 3D body shape and pose across different resolutions with a single model. Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation 因此 . However, manually annotating medical data is often laborious, and most existing learning-based approaches fail to accurately delineate object boundaries without effective geometric constraints. The dual form is used for global knowledge transfer, yielding a contrastive learning objective that maximizes the lower bound of mutual information between the teacher and the student networks. 関連論文リスト. Distillation的目标 ：在同类别的输出上让Student与Teacher靠近；在不同类别的输出上让Student和Teacher远离。. Enhancing the user experience is an essential task for application service providers. Yanbo Wang, Shaohui Lin*, Yanyun Qu, Haiyan Wu, Zhizhong Zhang, Yuan Xie*, Angela Yao. The dual form is used for global knowledge transfer, yielding a contrastive learning objective that maximizes the lower bound of mutual information between the teacher and the student networks. 71. Complementary Relation Contrastive Distillation. 刻画输出之间的差别不仅仅可以用cross entropy，其他的诸如MSE、KL divergence、JS divergence甚至Wasserstein distance . Align before Fuse: Vision and Language Representation Learning with Momentum Distillation. 2016).. Wasserstein Contrastive Representation Distillation. _:ID_b369261bbf1e2182c5245afaa9ee06c1 . contrastive representation distillation我们常常希望将表征性知识从一个神经网络转移到另一个神经网络。这方面的例子包括将一个大型网络提炼成一个较小的网络，将知识从一种感觉模式转移到另一种感觉模式，或者将一系列模型集合成一个单一的估计器。知识提炼是解决这些问题的标准方法，它使教师和 . Wasserstein Contrastive Representation Distillation Liqun Chen 1 , Zhe Gan 2 , Dong Wang , Jingjing Liu 2 , Ricardo Henao 1 , Lawrence Carin 1 1 Duke University, 2 Microsoft Dynamics 365 AI Research We present a new Multi-Proxy Wasserstein Classifier to imporve the image classification models by calculating a non-uniform matching flow between the elements in the feature map of a sample and multiple proxies of a class using optimal transport theory. Peng B, Jin X, Liu J, et al . Tao Yu and Shafiq Joty. Xuan Zhang . 【CVPR 2021】基于Wasserstein Distance对比表示蒸馏方法：Wasserstein Contrastive Representation Distillation论文地址：主要问题：主要思路：Wasserstein Distance：基本内容：定义：具体实现：Global Contrastive Knowledge Transfer：Local Contrastive Knowledge TransferUnifying Global and Local Knowledge Tran 引言 Knowledge Distillation在近几年得到了广泛的关注和研究。 . . Verma, P. K. Srijith, L. Carin and P. Rai, CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks, Neural . image-text pair. [8] uses OT in local contrastive knowledge distillation, where it directly minimizes the Wasserstein loss between student and teacher embeddings in a batch. The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a . (Image source: Noroozi, et al, 2017) Colorization#. PaperView - going over the paper Zhu, Jinguo, et al. Paper Code Poster Session 1. Wasserstein Contrastive Representation Distillation Liqun Chen1*, Dong Wang 1∗, Zhe Gan2, Jingjing Liu2, Ricardo Henao1, Lawrence Carin1 1Duke University, 2Microsoft Corporation {liqun.chen, dong.wang, ricardo.henao, lcarin}@duke.edu, {zhe.gan, jingjl}@microsoft.com Abstract The primary goal of knowledge distillation (KD) is to en- Existing work, e.g., using Kullback-Leibler divergence for distillation, may fail . (2021)" . [5:15] Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods. extend this idea with the Wasserstein dis-tance (Chen et al., 2020). arXiv:1910.02551; Wasserstein Contrastive Representation Distillation. Tian Y. , Krishnan D. and Isola P. , Contrastive representation distillation, arXiv preprint arXiv:1910.10699, (2019). Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent . Representation Consolidation for Training Expert Students [54.90754502493968] マルチヘッド多タスク蒸留法は,タスク固有の教師の表現を集約し,下流のパフォーマンスを向上させるのに十分であることを示す。 New Towards Compact Single Image Super-Resolution via Contrastive Self-distillation. Wasserstein Contrastive Representation Distillation, Liqun Chen, Dong Wang, Zhe Gan, Jingjing Liu, Ricardo Henao, Lawrence Carin, (CVPR 2021) Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models , Dong Wang , Yuewei Yang, Chenyang Tao, Zhe Gan, Liqun Chen, Fanjie Kong, Ricardo Henao, Lawrence Carin. The dual form is used for global knowledge transfer, yielding a contrastive learning objective that maximizes the lower bound of mutual information between the teacher and the student networks. Self-supervised learning in computer vision started from pretext tasks like rotation, jigsaw puzzles or even video ordering.All of these methods were formulating hand-crafted classification problems to . Ruiyuan Wu, Anna Scaglione, Hoi-To Wai, Nurullah Karakoc, Kari Hreinsson, Wing-Kin Ma. _:ID_b369261bbf1e2182c5245afaa9ee06c1 . The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student . Explosive growth — All the named GAN variants cumulatively since 2014. Credit: Bruno Gavranović So, here's the current and frequently updated list, from what started as a fun activity compiling all named GANs in this format: Name and Source Paper linked to Arxiv.Last updated on Feb 23, 2018. . Oral s 5:00-5:15. teachers with contrastive learning. 001 (2022-02-10) Graph-GAN A spatial-temporal neural network for short-term passenger flow prediction in urban rail transit systems. _:ID_b369261bbf1e2182c5245afaa9ee06c1 .

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